Capsule Networks. They were first introduced by Sabour et al. [1]} and since then a lot of work has been done, both to improve the routing mechanism and to build deeper models. Regarding the routing algorithm, Hinton et al. [2]} replace the dynamic routing with Expectation-Maximization, adopting matrix capsules instead of vector capsules. Wang et al. [3]} model the routing strategy as an optimization problem. Li et al. [4]} use master and aide branches to reduce the complexity of the routing process. Hahn et al. [5]} incorporates a self-routing method such that capsule models do not require agreements anymore. De Sousa Ribeiro et al. [6]} replace the routing algorithm with variational inference of part-object connections in a probabilistic capsule network, leading to a significant speedup without sacrificing performance. Since the CapsNet model introduced by Sabour et al. [1]} is a shallow network, several works attempted to build deep CapsNets. Rajasegaran et al. [8]} propose a deep capsule network architecture which uses a novel 3D convolution based dynamic routing algorithm aiming at improving the performance of CapsNets for more complex image datasets. Gugglberger et al. [9]} introduce residual connections to train deeper capsule networks.
